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检索条件"任意字段=International Conference on Data Science and Network Engineering, ICDSNE 2023"

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international conference on data science and network engineering, icdsne 2023

International Conference on Data Science and Network Enginee...

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international conference on data science and network engineering, icdsne 2023

ISBN: (纸本)9789819967544

The proceedings contain 39 papers. The special focus in this conference is on data science and network engineering. The topics include: Smart Surveillance System and Prediction of Abnormal Activity in ATM Using Deep Learning;a Framework for Extractive Text Summarization of Single Text Document in Tamil Language Using Frequency Based Feature Extraction Technique;an Approach to Mizo Language News Classification Using Machine Learning;BASiP: A Novel Architecture for Abstractive Text Summarization;a Hybrid Approach for Leaf Disease Classification Using Machine Learning and Deep Learning;enhancing Agricultural Decision-Making Through Machine Learning-Based Crop Yield Predictions;Pest Detection Using YOLO V7 Model;random Forest Classifier-Based Acute Lymphoblastic Leukemia Detection from Microscopic Blood Smear Images;FedCNNAvg: Federated Learning for Preserving-Privacy of Multi-clients Decentralized Medical Image Classification;training Algorithms for Mixtures of Normalizing Flows;acute Lymphoblastic Leukemia Detection Using DenseNet Model from Microscopic Blood Smear Images;a Disease Prediction Framework Based on Predictive Modelling;a data-Driven Diabetes Predictive Model Using a Novel Optimized Weighted Ensemble Approach;analysis of Image Caechniques Using Encoder–Decoder;security and Energy Efficiency Enhancement for the Internet of Things: Challenges, Architecture and Future Research;spot Pricing in Cloud Computing: A Comprehensive Survey of Mechanisms, Strategies, and Future Directions;A Comparative Analysis of Propagation Models Suitable for Non-Line-of-Sight 5G Communication at 26 GHz;validating δ-Currency Using Model Checking;a Batch-Service Queueing Assisted Blockchain System for Supply Chain Management;biFrost: A Blockchain-Based Decentralized Messaging Application;priority Based Load Balancing for Intercloud Computing Environments;machine Learning Approach to the Internet of Things Threat Detection.

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2nd international conference on data science and network engineering, icdsne 2024

2nd International Conference on Data Science and Network Eng...

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2nd international conference on data science and network engineering, icdsne 2024

ISBN: (纸本)9789819783359

The proceedings contain 29 papers. The special focus in this conference is on data science and network engineering. The topics include: Explainable AI Insights into a Time Series Weather Prediction Model Using Stacked LSTM;patient-Specific and Patient-Independent Seizure Prediction Using Ensemble Learning Technique;an Intuitive and Modular Framework for Enhanced Human-Machine Synergy;Wavelet-Transformed K-NN Pipeline for EEG-Based Eye Blink Classification with Time Wrapping;enabling Cursor Control Through Eye Movement Using Hidden Markov Model;Comparative Study: Word2Vec Versus TF-IDF in Software Defect Predictions;an Improved Deep Learning Framework Based on Multi-Scale Convolutional Architecture for Road Crack Detection;Knowledge Graph Relation Learning Using GAN-BERT;sightAssist: A Multi-facility Machine Learning Approach for Empowering the Visually Impaired;Predicting COVID-19 Cases in India Using ARIMA, Prophet, LSTM and data Analysis Using Power BI;Classification of Muti-Labeled Retinal Diseases in Retinal Fundus Images Using CNN Model ResNet18;LSTM-Based Portfolio Optimization with Gerber Covariance Estimator for Increased Robustness;handwritten Character Recognition from Small Grayscale Images Using Pre-trained Models;Forecasting Heart Disease Using Deep BI-DI Neural networks;emo-Tune: Harnessing Emotion-Based Music for Patient Wellness;Making data Secure in Detecting ADHD with Supervised Learning;an Analysis of Automatic Question Generation Research Progress and Challenges;a Federated Learning Approach Towards a Privacy-Preserving Technique for Brain Tumor Classification;low Cost Emergency Communication System for Disaster Affected Areas;a Slot-Integrated Based Partial Ground and Tapered Patch Antenna for Satellite Communications;a Review on Wireless Power Transfer Systems;application of Big data to Traffic Generated in Mechanisms Containment on Optical Burst Switching Distributed networks;a Real-Time Arm-Worn Sensor-Based Human Fall Alert Notification Mode

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Predicting COVID-19 Cases in India Using ARIMA, Prophet, LSTM and data Analysis Using Power BI 2nd

Predicting COVID-19 Cases in India Using ARIMA, Prophet, LST...

引用

2nd international conference on data science and network engineering, icdsne 2024

作者： Das, Abhrajit Seidenberg School of Computer Science and Information Systems Pace University New York United States

ISBN: (纸本)9789819783359

India confronted a serious challenge in the initial throes of the COVID-19 pandemic. This study addressed this by leveraging machine learning to analyse confirmed cases and support better decision-making. We compared the efficacy of Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet and Long Short-Term Memory (LSTM) models on a Kaggle dataset, visualizing the results with Power BI to forecast future trends. The LSTM model, evaluated using Mean Absolute Percentage Error (MAPE), demonstrated superior accuracy in predicting case numbers. This research underlines the importance of data analysis during public health emergencies. Machine learning offers valuable insights for policymakers, empowering them to control outbreaks and allocate resources effectively. Ultimately, this paves the way for strengthened public health responses and preparedness for future pandemics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Detection and Analysis of Cyber-Attacks on IoT network Devices 2nd

Detection and Analysis of Cyber-Attacks on IoT Network Devic...

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2nd international conference on data science and network engineering, icdsne 2024

作者： Adamu, Bashir Zak Kilincer, Ilhan Firat Ertam, Fatih Department of Digital Forensics Engineering Firat University Elazig Turkey

ISBN: (纸本)9789819783359

One of the most pressing concerns in network forensics is the detection of cyber-attacks in the Internet of Things (IoT) networks and their devices. Traditional intrusion detection systems based on signature rules are unable to detect current attack types. Hence, the need to urgently develop advanced methods for classifying IoT network traffic that can swiftly detect cyber-attacks becomes inevitable. This research aims to develop machine learning algorithms for cyber-attack detection in IoT-based networks, by analyzing the traffic data composed from the network itself. An ideal IoT network was implemented solely for the attack scenarios and generation of a dataset. For this study, both the structure and security of IoT networks were investigated in detail, by utilizing an IoT network created in real environment. Attack scenarios have been created for IoT devices in the real environment created. An IoT security data set was created by collecting the network flows obtained as a result of the attacks. On the created data set, classification results close to 100% accuracy values were obtained with machine learning algorithms. The data set obtained in the study has been published publicly so that other researchers can use it. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： network intrusion

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A Federated Learning Approach Towards a Privacy-Preserving Technique for Brain Tumor Classification 2nd

A Federated Learning Approach Towards a Privacy-Preserving T...

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2nd international conference on data science and network engineering, icdsne 2024

作者： De, Anurag Pal, Gautam Shyam, Karnam Tej, Kalakanti Pawan School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Computer Science and Engineering Department TIT Narsingarh Tripura Agartala India

ISBN: (纸本)9789819783359

The brain is one of the main organs in the human body. It controls almost all the actions of a human being. Any problem with the brain can lead to even fatal consequences. One of the most dangerous problems faced by the human brain is a brain tumor. If it is not treated properly at early stages, it may lead to severe conditions. However, finding a brain tumor is a very challenging task as the brain structure is very complex. Also, a lot of image data is required to train the model to detect brain tumors. However, the hospitals are not ready to share patient data to train the model. Hence, an approach to train the model by seeing the actual dataset or simply using decentralized data from multiple clients needs to be followed. So, a federated learning methodology is utilized to train the model. The trained federated learning model has achieved a training accuracy of 91% and a test accuracy of 88%. The federated learning and transfer learning approach stands out in preserving privacy, optimizing model performance, and leading to good collaboration among healthcare entities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Forecasting Heart Disease Using Deep BI-DI Neural networks 2nd

Forecasting Heart Disease Using Deep BI-DI Neural Networks

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2nd international conference on data science and network engineering, icdsne 2024

作者： Bhuvanya, R. Kujani, T. Matheswaran, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Tamil Nadu Chennai600016 India VelTech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India K. Ramakrishnan College of Technology Samayapuram Tamil Nadu Tiruchirappalli621112 India

ISBN: (纸本)9789819783359

Globally, heart disease continues to claim countless lives, remaining a top killer, highlighting the need for accurate prediction models to aid in early diagnosis and intervention. To enhance heart attack prediction and improve patient outcomes, this study investigates the efficacy of a deep Bi-Directional Recurrent Neural network architecture. This novel approach leverages stacked Long Short-Term Memory (LSTM) units to capture temporal dependencies within medical data, potentially surpassing the accuracy of conventional Machine Learning (ML) algorithms in predicting heart attacks. The results highlight that the twin Long Short-Term Memory model achieved an exceptional accuracy of 98% in predicting heart attacks, surpassing other conventional ML algorithms. The results highlight the significance of utilizing sophisticated machine learning techniques in the medical field to enhance patient results and decrease the prevalence of cardiovascular illnesses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Computer Vision and Deep Learning-Based Model for Detecting Spoofed Faces in Images 2nd

Computer Vision and Deep Learning-Based Model for Detectin...

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2nd international conference on data science and network engineering, icdsne 2024

作者： Salian, Gayathri P Rao, Manasa K Rashmi, M. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819783359

In the modern era of smart applications, video data is critically important in various contexts. In most of these applications, cameras are frequently incorporated to facilitate authentication. As a result, face recognition is the biometric method most frequently employed to authenticate users in these applications. The vulnerability of face recognition systems to spoofing attacks grows in tandem with their increased usage. As a result, robust countermeasures are required. This paper presents an approach to face anti-spoofing through transfer learning and YOLOv8 optimization. Additionally, a custom dataset was constructed using images obtained from web cameras and an existing dataset to assess the proposed work’s real-time effectiveness. The proposed approach also adds a blurriness threshold during image capture to improve performance. With a mean Average Precision (mAP50) of 0.975, the experimental outcomes highlight the model’s effectiveness in detecting face spoofing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biometrics

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Knowledge Graph Relation Learning Using GAN-BERT 2nd

Knowledge Graph Relation Learning Using GAN-BERT

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2nd international conference on data science and network engineering, icdsne 2024

作者： Jain, Neelam Mehta, Krupa SVKM’s Mithibai College of Arts Chauhan Institute of Science & Amrutben Jivanlal College of Commerce And Economics Mumbai India Faculty of Computer Application and IT GLS University Gujarat Ahmedabad India

ISBN: (纸本)9789819783359

Massive knowledge graphs (KGs) are increasingly significant in modern information systems. Previous research on knowledge graph completion must gather sufficient training instances for new introduced relations in order to increase the coverage of KGs. This study examines an innovative approach to the scarce labelling problem. The model use text descriptions to learn semantic elements of newly added relations, allowing us to recognise facts even when there are few examples available. To learn relations based on text, we utilised a GAN-BERT model, which is a modified form of BERT. Four distinct datasets are employed for this. Experiments demonstrate that using GAN-BERT decreases the demand for annotated instances and improves performance in various relation prediction tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Application of Crawler Algorithm for Situational Awareness in network Security 5th

Application of Crawler Algorithm for Situational Awareness i...

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5th international conference on data science, Machine Learning and Applications

作者： Jagadish, Sripelli Madanan, Mukesh SR Univ Sch CS & AI Warangal Telangana India Dhofar Univ Salalah Dept Comp Sci Salalah Oman

ISBN: (纸本)9789819780334;9789819780310;9789819780303

A key idea in network security is situational awareness, which enables prompt detection and reaction to cybersecurity threats. This study investigates the use of crawler algorithms to improve situational awareness in network security. In this article, we look at the theoretical underpinnings of awareness of situations, the function of crawler algorithms, how they're used in security of networks, and how they affect threat and incident response. The network protection event database was created and constructed using data gathered from multiple Zhiming network security incident websites using the Scrappy web crawler framework, which improved the situational awareness research's data. In this study, a text-based network safety event evaluation instrument is built and executed in order to analyze the evaluation and interpretation of network security incidents, which is a critical component in the process of security awareness and perception. The crawler technique was used to build a network vulnerability event database that has 45,820 sets of data, which is an increase in capacity of 14.36% and 30.53% over the old approach and a reduction in reading time of 65.2% and 88.4%.

关键词： Crawler algorithm network security Situational Awareness

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Decentralized Energy Grid System Using IoT and Blockchain: A Sustainable Future 2nd

Decentralized Energy Grid System Using IoT and Blockchain: A...

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2nd international conference on data science and network engineering, icdsne 2024

作者： Debnath, Saswati Patil, Vedavati Agrawal, Dhruv Department of Computer Science and Engineering Alliance University Karnataka Bengaluru India

ISBN: (纸本)9789819783359

The global pursuit of sustainable energy solutions has led to the exploration of innovative technologies aimed at revolutionizing energy management and distribution. This paper presents integration of Blockchain and Internet of Things (IoT) technologies in decentralizing energy management with a focal point on transmitting power data from small-scale renewable power stations to a central power plant. IoT devices strategically monitor energy production from renewable energy sources, such as wind turbines, solar panels, and microgrids, enabling real-time data analysis to maximize efficiency. These transactions, which are automated by smart contracts, ensure trust. Interoperability, data security, and user interfaces are all covered as the technical architecture required for seamless integration in this paper. It is important that regulations need to be changed to make room for creative energy solutions. This framework highlights sustainability, encourages energy independence, and lessens dependency on centralized power by supporting renewable energy sources. By combining blockchain technology, IoT, and sustainable energy practices, the proposed system aims to transform the way of energy distributing, improving security, resilience, and sustainability while giving to communities more control over how much energy they use and produce. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clean energy

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